Bearing Fault Diagnosis Method Based on Down-Sampling in Frequency Domain and CNN

被引：0
|
作者：
Zhou X. [1 ]
Mao S. [1 ]
Li M. [1 ]
机构：
[1] Institute of Remote Sensing and Geographical Information System, Peking University, Beijing
来源：
Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis | 2023年 / 59卷 / 02期
关键词：
bearing fault diagnosis; convolutional neural network (CNN); deep learning; multiple working conditions; strong noise;
D O I：
10.13209/j.0479-8023.2022.098
中图分类号：
学科分类号：
摘要：
In the industrial field, the original fault signals collected during the operation of the equipment have the characteristics of strong noise and multiple working conditions. Most of previous data-driven fault diagnosis methods for bearings have relatively weak anti-noise ability and generalization ability. To solve these problems, a novel bearing fault diagnosis method based on down-sampling in frequency domain and convolutional neural network (CNN), called Ds-CNN, is proposed. Down-sampling in frequency domain consists of maximum down-sampling with bias and noise transverse truncation, which can realize data augmentation, reduce the difference between samples in frequency domain, and reduce the influence of noise on signals in frequency domain. The CNN model based on frequency domain signals can automatically extract fault features from signals after down-sampling and complete the identification and classification of bearing faults. The results of the experiment show that Ds-CNN has higher recognition accuracy than common models under strong noise environment and multiple working conditions. © 2023 Peking University. All rights reserved.
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页码：251 / 260
页数：9
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